Goals for these graphs
#import libraries
import pandas as pd
import altair as alt
alt.renderers.enable('notebook')
alt.data_transformers.enable('default', max_rows=None)
from sklearn.feature_extraction.text import TfidfVectorizer
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import ast
#import data
Datasets: National Publications House
Egyptian Periodicals
AAPSO Periodicals
African American Periodicals
# 1. Arab Observer 1960-1963
ArabObserver_df = pd.read_csv('../data/arab_observer_corpus_cleaned.csv')
ArabObserver_df.datetime = pd.to_datetime(ArabObserver_df['date'], format='%Y-%B-%d', errors='coerce')
ArabObserver_1960_61 = ArabObserver_df[ArabObserver_df.datetime < '1962-01-01']
alt.Chart(ArabObserver_1960_61).mark_bar().encode(
x=alt.X('date:T'),
y=alt.Y('sum(cleaned_spacy_counts)')
)